다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Fake News Detection on Social Media: A Data Mining Perspective

What is Twitter

As part of an automated fact-checking pipeline, the claim veracity classification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evidence pairs leads to a scarcity of datasets, particularly when dealing with new domains. In this paper, we introduce SEED, a novel vector-based method to few-shot claim veracity classification that aggregates pairwise semantic differences for claim-evidence pairs. We build on the hypothesis that we can simulate class representative vectors that capture average semantic differences for claim-evidence pairs in a class, which can then be used for classification of new instances. We compare the performance of our method with competitive baselines including fine-tuned BERT/RoBERTa models, as well as the state-of-the-art few-shot veracity classification method that leverages language model perplexity. Experiments conducted on the FEVER and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available.

Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification

Scientific claim verification is a unique challenge that is attracting increasing interest. The SCIVER shared task offers a benchmark scenario to test and compare claim verification approaches by participating teams and consists in three steps: relevant abstract selection, rationale selection and label prediction. In this paper, we present team QMUL-SDS's participation in the shared task. We propose an approach that performs scientific claim verification by doing binary classifications step-by-step. We trained a BioBERT-large classifier to select abstracts based on pairwise relevance assessments for each and continued to train it to select rationales out of each retrieved abstract based on . We then propose a two-step setting for label prediction, i.e. first predicting "NOT_ENOUGH_INFO" or "ENOUGH_INFO", then label those marked as "ENOUGH_INFO" as either "SUPPORT" or "CONTRADICT". Compared to the baseline system, we achieve substantial improvements on the dev set. As a result, our team is the No. 4 team on the leaderboard.

QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification

Since very recently, users on the social bookmarking service Delicious can stack web pages in addition to tagging them. Stacking enables users to group web pages around specic themes with the aim of recommending to others. However, users still stack a small subset of what they tag, and thus many web pages remain unstacked. This paper presents early research towards automatically clustering web pages from tags to nd stacks and extend recommendations.

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Stacking from Tags: Clustering Bookmarks around a Theme

In this paper we present a study on semi-supervised multiclass web page classification using SVM. Due to the binary and supervised nature of the classical SVM algorithms, and trying to avoid complex optimization problems, we propose an approach based on the combination of classifiers, not only binary semi-supervised classifiers but also multiclass supervised ones. The results of our experiments over three benchmark datasets show noticeably higher performance for the combination of multiclass supervised classifiers. On the other hand, we analyze the contribution of unlabeled documents during the learning process for these environments. In our case, and unlike for binary tasks, we get higher eectiveness for multiclass tasks

/pdf/comparativa-de-aproximaciones-a-svm-semisupervisado-5ad1t31nqz.pdf

Comparativa de Aproximaciones a SVM Semisupervisado Multiclase para Clasificacion de Paginas Web A Comparison of Approaches to Semi-supervised Multiclass SVM for Web Page Classification

Cases of organised disinformation campaigns on Twitter, including those reported by the social network itself in its Transparency centre, continue unabated. The negative consequences of these attacks in processes of great importance to societies, such as electoral processes or vaccination campaigns, have sparked research into detecting this type of malicious user. State-of-the-art models for bot detection use numerous information collected from profiles, tweets, or network archi-tecture to obtain competitive outcomes. On the other hand, these models allow for post-hoc detection of such users because they rely on fixed training datasets to classify users based on their previous activities. In contrast, we propose a proactive technique that uses user records to predict dangerous attacks before they occur as a measure to make social networks safer, fairer and less biased. For this purpose, our method uses a model that predicts malicious assaults by projecting users' embedding trajectories before completing their actions. We employed a Dynamic Directed Multigraph representation of temporal inter-actions between people in the Twittersphere for the experiments. By comparing them in the same data, our model outperforms state-of-the-art methods by 40.66% in F-score detecting malicious users preemptively. In addition, we propose a model selection study that evaluates the usefulness of several system components.

Arkaitz Zubiaga

Papers

Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification

QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification

Stacking from Tags: Clustering Bookmarks around a Theme

Comparativa de Aproximaciones a SVM Semisupervisado Multiclase para Clasificacion de Paginas Web A Comparison of Approaches to Semi-supervised Multiclass SVM for Web Page Classification

Achieving Participatory Smart Cities by Making Social Networks Safer